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ORIGINAL RESEARCHpublished: xx February 2019

doi: 10.3389/fphar.2019.00200

Edited by:Andrew Zloza,

Rush University Medical Center,United States

Reviewed by:Praveen Bommareddy,

Rutgers, The State Universityof New Jersey, United States

Tomas Garzon-Muvdi,Thomas Jefferson University

Hospitals, United States

*Correspondence:Derek A. Wainwright

Derekwainwright@northwestern.edu

Specialty section:This article was submitted to

Cancer Molecular Targetsand Therapeutics,

a section of the journalFrontiers in Pharmacology

Received: 12 January 2019Accepted: 18 February 2019Published: xx February 2019

Citation:Ladomersky E, Scholtens DM,

Kocherginsky M, Hibler EA,Bartom ET, Otto-Meyer S, Zhai L,

Lauing KL, Choi J, Sosman JA,Wu JD, Zhang B, Lukas RV and

Wainwright DA (2019) TheCoincidence Between Increasing Age,

Immunosuppression,and the Incidence of Patients With

Glioblastoma.Front. Pharmacol. 10:200.

doi: 10.3389/fphar.2019.00200

The Coincidence Between IncreasingAge, Immunosuppression, and theIncidence of Patients WithGlioblastomaErik Ladomersky1, Denise M. Scholtens1,2, Masha Kocherginsky2,3, Elizabeth A. Hibler2,Elizabeth T. Bartom4, Sebastian Otto-Meyer1, Lijie Zhai1, Kristen L. Lauing1,Jaehyuk Choi4,5, Jeffrey A. Sosman6, Jennifer D. Wu7,8, Bin Zhang6,8, Rimas V. Lukas9,10

and Derek A. Wainwright1,6,8,9*

1 Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, United States,2 Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States,3 Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL,United States, 4 Department of Biochemistry and Molecular Genetics, Northwestern University Feinberg School of Medicine,Chicago, IL, United States, 5 Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, IL,United States, 6 Department of Medicine-Hematology and Oncology, Northwestern University Feinberg School of Medicine,Chicago, IL, United States, 7 Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL,United States, 8 Department of Microbiology-Immunology, Northwestern University Feinberg School of Medicine, Chicago,IL, United States, 9 Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine,Chicago, IL, United States, 10 Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL,United States

Background: Glioblastoma (GBM) is the most aggressive primary brain tumor in adultsand is associated with a median overall survival (mOS) of 16–21 months. Our previouswork found a negative association between advanced aging and the survival benefit aftertreatment with immunotherapy in an experimental brain tumor model. Given the recentphase III clinical success of immunotherapy in patients with many types of cancer, butnot for patients with GBM, we hypothesize that aging enhances immunosuppressionin the brain and contributes to the lack of efficacy for immunotherapy to improvemOS in patients with malignant glioma. Herein, we compare epidemiological data forthe incidence and mortality of patients with central nervous system (CNS) cancers,in addition to immune-related gene expression in the normal human brain, as well asperipheral blood immunological changes across the adult lifespan.

Methods: Data were extracted from the National Cancer Institute’s surveillance,epidemiology, and end results (SEER)-, the Broad Institute’s Genotype TissueExpression project (GTEx)-, and the University of California San Francisco’s 10kImmunomes-databases and analyzed for associations with aging.

Results: The proportion of elderly individuals, defined as ≥65 years of age, haspredominantly increased for more than 100 years in the United States. Over time, therise in elderly United States citizens has correlated with an increased incidence andmortality rate associated with primary brain and other CNS cancer. With advancedaging, human mRNA expression for factors associated with immunoregulationincluding immunosuppressive indoleamine 2,3 dioxygenase 1 (IDO) and programmed

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death-ligand 1 (PD-L1), as well as the dendritic cell surface marker, CD11c,increase in the brain of normal human subjects, coincident with increased circulatingimmunosuppressive Tregs and decreased cytolytic CD8+ T cells in the peripheral blood.Strikingly, these changes are maximally pronounced in the 60–69 year old group;consistent with the median age of a diagnosis for GBM.

Conclusion: These data demonstrate a significant association between normal humanaging and increased immunosuppression in the circulation and CNS; particularly late inlife. Our data raise several hypotheses including that, aging: (i) progressively suppressesnormal immunosurveillance and thereby contributes to GBM cell initiation and/oroutgrowth; (ii) decreases immunotherapeutic efficacy against malignant glioma.

Keywords: aging, biomarker, IDO, immunosuppression, PD-L1, immunotherapy, Treg, IDH

INTRODUCTION

Glioblastoma (GBM) is the most common primary malignantbrain tumor in adults. Despite the aggressive standard of careregimen that includes maximal surgical resection followed byradiation therapy and chemotherapy with temozolomide, andmore recently tumor treating fields, the median overall survival(mOS) remains at 16–21 months post-diagnosis, with just43% of patients surviving for 2 years post-diagnosis (Stuppet al., 2005, 2009, 2017; Johnson and O’Neill, 2012). A majorfactor contributing to the poor GBM patient prognosis is thepotent immunosuppression, found systemically and locally inthe brain tumor microenvironment (Chongsathidkiet et al.,2018). High intratumoral expression of immunosuppressivemediators including programmed cell death protein-1 (PD-1)and indoleamine 2,3 dioxygenase 1 (IDO), is prognostic fordecreased GBM patient survival (Wainwright et al., 2012; Nduomet al., 2016; Zhai et al., 2017). Immune checkpoint inhibitortreatment has demonstrated a survival benefit in patients withnon-small cell lung carcinoma (Antonia et al., 2016), renal cellcancer (Motzer et al., 2015), end-stage melanoma (Larkin et al.,2015), and other aggressive malignancies arising outside of thecentral nervous system (CNS). In contrast, this benefit has yet totranslate into patients with GBM in phase III clinical trials to-date(Bristol-Myers Squibb, 2017; Filley et al., 2017).

Age is one of the primary risk factors for cancer, withindividuals ≥65 years of age accounting for 60% of newlydiagnosed malignancies and 70% of all cancer-related deaths(Ries et al., 2006). A similar report highlighted an age adjustedcancer mortality rate for persons ≥65 at ∼16 times higherthan the mortality rate for those <65 (Berger et al., 2006).Care for these individuals is challenging due to the number ofdiseases elderly subjects are at high risk for, which also raises thelikelihood of presenting multiple comorbidities during advancedaging (Yancik et al., 1998, 2001). Similarly, the incidence andmortality rate of GBM increases during advanced aging witha median diagnosis at 64 years old (Young et al., 2017).Aging is a complex process that affects nearly all aspectsof the immune system (Nikolich-Zugich, 2018). In general,advanced aging decreases immune system effectiveness, as isevidenced in elderly individuals who receive the influenza vaccine

(McElhaney and Dutz, 2008). Aging also negatively impactsapoptotic cell clearance (Aprahamian et al., 2008), the numbersof naïve T cells (Cambier, 2005), and the wound healing response(Wick et al., 2010). T cell senescence increases with progressiveaging through the induction of p16 (Liu et al., 2009). Aging alsoaffects T cell receptor (TCR) signaling in CD4+ T cells, duein part to decreased miR-181a; a microRNA highly expressedin normal T cells (Adachi and Davis, 2011; Li et al., 2012;Chen et al., 2013). Both aged mice (Rossi et al., 2005) andhuman (Pang et al., 2011) hematopoietic stem cells (HSC)possess a myeloid-biased differentiation potential as comparedwith HSC from young subjects. Moreover, macrophages fromdonor subjects with advanced age possess decreased capacityfor antigen presentation as compared to young donors (Davilaet al., 1990; Gon et al., 1996). Neuro-immune interactions arealso affected by aging in the brain (Carson et al., 2006) thatinclude a significant upregulation of MHCII and CD11b onmicroglia (Rogers et al., 1988; Perry et al., 1993), as well as anaccumulation of brain-resident dendritic cells (Bulloch et al.,2008; D’Agostino et al., 2012; Kaunzner et al., 2012). However,it is not yet clear as to whether aging possesses a distinct impacton immunosuppressive gene expression across select tissues thatcontribute to a microenvironment permissive for oncogenesis,tumor progression, and/or resistance to immunoregulatory-based therapies.

Our laboratory previously demonstrated a negative impactof advanced age on the survival of animals engrafted withsyngeneic experimental brain tumors (Ladomersky et al., 2016).C57BL/6 mice intracranially injected with murine GL261 gliomacells at 72–74 weeks old, which is similar in humans to atime frame associated with the median age of a GBM patientdiagnosis (Fox et al., 2007; Dutta and Sengupta, 2016), havea decreased mOS as compared to animal subjects with an ageof 6–8 weeks old (27.5 and 21.5 days, respectively, P = 0.029,n = 10–12/group); the latter of which is similar in age toa human teenager. The negative impact of advanced agingwas coincident with increased immunosuppressive IDO1 geneexpression in the normal, non-malignant mouse brain. Morerecently, we discovered that a substantial proportion of C57BL/6mice intracranially injected with GL261 at 6–8 weeks of ageexperience long-term survival when simultaneously treated with

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radiation (RT), anti-PD-1 mAb, and IDO1 enzyme inhibitor(Ladomersky et al., 2018). The brain tumor survival benefitprovided by this treatment, however, was negatively affected byanimal subjects with advanced age as compared with youngsubjects (Ladomersky et al., 2018). Importantly, there was nosignificant difference in tumor infiltrating leukocyte populationsbetween the young and aged subjects within treatment groups. Toour knowledge, this is the first preclinical primary brain cancerstudy to demonstrate a negative impact of aging on survival aftertreatment with immunotherapy.

Further supporting the hypothesis that, advanced agingmediates suppression of immune system efficacy against atumor challenge event, previous work showed that splenocytesisolated from young, but not old immunized subjects, wereable to eradicate subcutaneous tumors in mice (Schreiber et al.,2012). Specifically, immunodeficient recombination activatinggene knockout mice (Rag−/−) were subcutaneously engrafted8101 cells arising from mice treated with UV-irradiation,and possessing a somatic mutation in the T cell-recognizedantigen RNA helicase, p68. Splenocytes isolated from 5 monthold mice and immunized with live 8101 cells, but notthose from immunized 29 month old mice, eradicated 8101cell-based tumors post-adoptive transfer into Rag−/− mice.Interestingly, melanoma patients≥62 years of age show increasedresponsiveness to anti-PD-1 mAb treatment as compared withyounger human subjects (Kugel et al., 2018). Recapitulating thisclinical observation, 10 month old animal subjects, which roughlycorrelate to the human age of 38–47 years and engrafted withmurine BSC9AJ2 melanoma cells, show decreased tumor growthas compared to 2 month old engrafted mice after treatment withanti-PD-1 mAb (Kugel et al., 2018). This highlights an interestingdichotomy suggesting that, the productivity of an anti-tumorimmune response during treatment with immunotherapy likelydepends on both the cancer type and age of the host. Thesecombined findings may suggest that GBM is an outlier whenconsidering its place in cancer immunology and immunotherapy.Accordingly, we previously found an inverse association betweenhigh CD3ε and CD8α gene expression with GBM patient survival(Zhai et al., 2017), which is a diametrically opposite finding ascompared with non-small cell lung cancer and melanoma (Zenget al., 2016; Zhai et al., 2018).

In our current study, we explored the associations betweenhuman: (i) aging; (ii) levels of gene expression associatedwith immunoregulation inside the brain; (iii) immunologicalchanges in the peripheral blood; and (iv) incidence andmortality of patients with primary brain and other CNS tumors.Epidemiological analyses of GBM patient characteristics werecompared across the Surveillance, Epidemiology, and End Results(SEER) database, age-dependent gene expression levels of normalhuman brain from the GTEx database, and age-associatedchanges in normal human peripheral blood leukocytes fromthe 10k Immunomes database. Our study confirms the strikingobservation that the brain cancer mortality rate is actively rising,with a particular enrichment among the elderly population inthe United States. The incidence of GBM is 3.4× higher amongindividuals ≥65 years old, as compared to those <65, whilethe mortality rate for individuals ≥65 years old with GBM is

7× higher as compared to GBM patients <65. Gene expressionlevels of immunosuppressive IDO1 increased in the normalhuman brain and was maximal among individuals aged 60–69 years old. As compared to younger subjects, there was amaximal incidence of circulating immunosuppressive regulatoryT cells (Tregs) and a significantly decreased cytolytic CD8+ Tcell population among the 60–69 year old age group. Our studyfound a cumulative peak index for immunosuppressive and/orimmunoregulatory mediators during the time frame associatedwith the median age of a GBM patient diagnosis. These dataraise the intriguing possibility that aging suppresses mechanismsof immunosurveillance and responsiveness to immunotherapy,which is associated with the increasing number of elderly patientswith brain cancer and the failure of immunotherapy to benefitGBM patients in phase III clinical trials to-date, respectively.

MATERIALS AND METHODS

DataLife Expectancy DataLife expectancy data were analyzed from the Center for DiseaseControl and Prevention. Data were accessed through the NationalCenter for Health Statistics portal1. The filename used was Lifeexpectancy at birth and at age 65, and at age 75, by sex, race, andHispanic origin: United States, selected years 1900–2016.

Surveillance of Epidemiology and End ResultsDatabase (SEER)All population, incidence, and mortality data from the SEERdatabase were accessed through SEER∗Stat (Version 8.3.52).Population-level data were accessed through a Frequency Session.Variables examined were: (1) Age recode with <1 year olds; and(2) year. Incidence and mortality data were accessed through aRate Session. Variables examined for incidence include: (1) agerecode with <1 year olds; (2) year of diagnosis; (3) histologyrecode – broad groupings; (4) histology recode – brain groupings;and (5) COD to site recode. The COD to site recode was usedto analyze the mortality rate of GBM. Variables examined formortality include: (1) age recode with <1 year old; (2) yearof death; and (3) cause of death recode. All rate data werecrude/non-age-adjusted. Data were accessed on 12-05-2018.

NCI 2018 EstimatesEstimated incidence and mortality data were retrieved from theNCI Cancer Stat Facts website3. The top 16 causes of cancer wereanalyzed for both incidence and mortality. Comparison acrossthe 16 cancers for the mortality to incidence ratio estimate werecalculated by dividing the estimated mortality with the estimatedincidence in the year 2018. Cost data were accessed through theNCI Cancer Prevalence and Cost of Care Projections website4.Cost per patient was calculated by taking the 2018 estimated

1https://www.cdc.gov/nchs/hus/contents2017.htm#0152https://seer.cancer.gov/seerstat/3https://seer.cancer.gov/statfacts/html/common.html4https://costprojections.cancer.gov/graph.php#

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total charges for each type of cancer and dividing it by the 2018estimated incidence of each cancer type.

TCGASurvival data for GBM patients were analyzed from the cancergenome atlas. The data were accessed using the UCSC Xenaportal. Data were accessed on 02-01-2019.

10k ImmunomesNormal human peripheral blood cell data were analyzed from the10k Immunomes database. The data were accessed through theUCSF portal5. All data analyzed were CyTOF data plotted by age.Data were accessed on 06-22-2018.

GTExGene expression data for normal human tissues were analyzedfrom the GTEx database. Data were accessed through the dbGaPportal. All data were represented as log-transformed fragmentsper kilobase of transcript per million mapped reads (FPKM).These data were then plotted by subject age.

Statistical Analysis10k Immunomes and TCGA expression data are represented asthe mean ± SEM. GTEx data are represented by each individualvalue. The statistical significance of differences in cell counts(CyTOF) and gene expression (log-transformed FPKM) betweentwo groups was determined by Student t-test. Differences amongmultiple groups were assessed using ANOVA with post-hocTukeytest. The statistical significance of TCGA survival data wasdetermined by Log-rank test. Data were analyzed using Prismsoftware (GraphPad Software). A P-value less than 0.05 wasconsidered significant.

RESULTS

The Rate of Cancer Incidence IncreasesWith AgeOver the past 40 years in the United States, improved healthcaresystems and enhanced awareness of proper nutrition and exercisehave led to substantial increases in mean life expectancy, risingfrom 72.6 years of age in 1975, to 78.8 years of age in 2015(Figure 1A). Coincident with the increasing life expectancy in theUnited States, the proportion of elderly (≥65 years) individualsis rising. The elderly age group represented 10.6% of the totalpopulation in 1975, and increased to 14.9% in 2015 (Figure 1B).Both the incidence and mortality rates associated with cancerdiagnoses are higher among the elderly population as comparedto individuals <65 years of age (Figures 1C,D). In the year 1975,the incidence and mortality rate within the elderly populationfor all malignancies was 1,732/100,000 people and 942/100,000,respectively, whereas in the year 2015, the incidence rate for allmalignancies in the elderly population was 1,876/100,000 andthe mortality rate was 879/100,000. Compared to the population<65, these incidence and mortality rates were 9.6 and 12.1

5http://10kimmunomes.ucsf.edu/

FIGURE 1 | Progressive aging is associated with an increased rate of cancerincidence and mortality. (A) Life expectancy from the Center for DiseaseControl and Prevention (CDC) database for an average lifespan in theUnited States between the years 1975 and 2015. (B) Population data fromthe Surveillance, Epidemiology, and Ends Results (SEER) databaserepresenting the total number of elderly human subjects (ages ≥65) divided bythe total population in the United States for the years 1975, 1995, and 2015.(C) Incidence and (D) mortality rates from the SEER database for allmalignancies in the years 1975, 1995, and 2015. Rates are defined asnumber of cases and deaths, respectively, divided by the population in eachage category and multiplied by 100,000 people (per 100,000).

times higher, respectively, in 1975, and 7.1 and 13.6 timeshigher, respectively, in 2015. The slight decrease in mortalityrate between 1975 and 2015 is most likely due to improveddetection and treatment techniques in select cancers. However,due to the increasing elderly population, the absolute numbersof cancer related mortalities in this population has risen from214,173 in 1975, to 419,389 in 2015. Together, these data suggestan association between the aging population and an absoluteincrease of cancer-related deaths in the elderly population ofthe United States.

Brain Cancer Diagnoses Pose a GrowingChallenge to the United StatesHealthcare SystemPrimary brain tumors arising from a transformed cell within theCNS is a relatively rare form of cancer, with the 16th highestrate of incidence among all cancers, and an estimated 23,880 newpatient diagnoses in 2018 (Figure 2A). Dwarfing this numberis the estimated 266,120 new diagnoses for patients with breastcancer in the year 2018. The overall ratio of estimated breast

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cancer incidence, as compared with brain cancer, is 11:1. Incontrast, the estimated mortality rate for patients diagnosed withCNS cancer is 16,830 in 2018 (Figure 2B), and is 40,920 forindividuals diagnosed with breast cancer. The overall ratio ofbreast cancer-associated mortalities, as compared to mortalitydue to CNS cancer, is only 2.4:1. Aggressiveness of cancerdiagnoses can also be calculated with the mortality-to-incidenceratio (MIR), which is determined by dividing overall mortalitywith the number of new diagnoses in a given year (Choi et al.,2017). CNS cancer as a group possesses the third highest MIR of0.70, which is only exceeded by pancreatic and liver cancer, withMIR scores of 0.80 and 0.72, respectively (Figure 2C). Leading allother groups, treatment for CNS cancers is the most costly on aper patient basis, with an average cost estimated to be $225,364 in2018 (Figure 2D).

Brain Cancer Incidence/Mortality IsRising and Enriched in the ElderlyAlthough there has only been a small change in the incidenceand mortality rates of all malignancies (Figures 1C,D) from 1975

FIGURE 2 | Brain andQ1 other CNS cancer is associated with a highmortality/incidence ratio and is expensive to treat. The estimated total (A)incidence and (B) mortality rate for the top 16 cancer types according to NCICancer Stat Facts. (C) The calculated mortality to incidence ratio (estimatedmortality/estimated incidence) for the top 16 cancer sites according to NCICancer Stat Facts. The dotted line represents an average across all 16 cancertypes. (D) Cost (United States dollars) per patient for each of the top 16cancer types. Dotted line represents the average for all 16 cancer types. NR,Not reported.

to 2015, analysis of incidence data for brain and other CNScancer shows a different trend (Figure 3A). The incidence ratehas increased overall from 5.4/100,000 in 1975, to 7.0/100,000 in2015. The overall increase, however, is primarily attributable tothe elderly population. In 1975 the incidence rate for individualswith brain and other CNS malignancies ≥65 years of age was14.2/100,000, which increased nearly 37% to 19.4/100,000 in2015. In that same time span, the incidence rate for those<65 years of age only slightly increased from 4.4/100,000 in1975 to 5.0/100,000 in 2015. When comparing other malignancytypes including breast, pancreatic, and lung, a similar trendof increased incidence was observed. Breast cancer incidencerates increased from 47.4/100,000 to 78.0/100,000 in the totalpopulation over the same time span, and from 192.4/100,000to 242.6/100,000 among the elderly population (Figure 3B).Pancreatic cancer incidence rates rose more slightly, from9.4/100,000 to 14.5/100,000 in the total population and from63.3/100,000 to 68.8/100,000 among the elderly over the past40 years (Figure 3C). The incidence of melanoma increased from6.8/100,000 to 28.4/100,000 in the total population and from16.7/100,000 to 97.7/100,000 within the elderly population from1975 to 2015 (Figure 3D). Overall, more people were diagnosedwith malignancies in 2015 as compared with 1975.

FIGURE 3 | Incidence rate for brain, breast, pancreatic, and melanomamalignancies over time. Incidence rates for (A) brain and other CNS, (B)breast, (C) pancreatic, and (D) melanoma malignancies analyzed from theSEER database for the years 1975, 1995, and 2015. Rates defined asnumber of cases divided by the population in each age category multiplied by100,000 people (per 100,000).

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Similar to the increased rate of incidence, the overall mortalityrate among individuals with brain and other CNS cancersincreased in the total population from 3.8/100,000 in 1975to 5.1/100,000 in 2015 (Figure 4A) The mortality rate forindividuals with brain and other CNS malignancies≥65 years oldwas 11.6/100,000 in 1975, and increased by 56% to 18.1/100,000in 2015. In contrast, the mortality rate for individuals <65 yearsof age rose slightly from 2.8/100,000 in 1975 to 2.9/100,000 in2015. The overall mortality rate for individuals with breast cancerdecreased from 15.1/100,000 in 1975 to 13.1/100,000 in 2015, andfrom 66.8/100,000 to 52.9/100,000 among individuals ≥65 yearsof age during the same time periods, respectively (Figure 4B).The overall mortality rate for individuals with pancreatic cancerrose from 9.0/100,000 to 13/100,000 from 1975 to 2015, butonly increased slightly from 56.2/100,000 to 63.0/100,000 amongindividuals ≥65 years of age during the same time periods,respectively (Figure 4C). The overall mortality rate increased forpatients with melanoma from 1.8/100,000 to 2.8/100,000 from1975 to 2015 and increased from 6.2/100,000 in 1975 to 12.2 in2015 among individuals ≥65 years of age during the same timeperiods, respectively (Figure 4D).

FIGURE 4 | Mortality rate for brain, breast, pancreatic, and melanomamalignancies over time. Mortality rates for (A) brain and other CNS, (B)breast, (C) pancreatic, and (D) melanoma malignancies analyzed from theSEER database for the years 1975, 1995, and 2015. Rates defined asnumber of deaths divided by the population in each age category multipliedby 100,000 people (per 100,000).

When analyzing the MIR as a function of aging, there weredifferences in trends among the malignancies analyzed. While theoverall MIR for individuals with brain cancer increased slightlyfrom 0.70 in 1975 to 0.73 in 2015 (Figure 5A), there is a largerincrease from 0.82 in 1975 to 0.93 in 2015 among individuals≥65. In contrast, the overall MIR for individuals with breastcancer decreased over the same time span from 0.32 in 1975to 0.17 in 2015 (Figure 5B), with a similar decrease of 0.35in 1975 to 0.22 in 2015 among individuals ≥65. The overallMIR for individuals with pancreatic cancer was 0.96 in 1975and slightly decreased to 0.90 in 2015 (Figure 5C), whereas thisfigure is relatively unchanged within the elderly population at0.89 in 1975 to 0.90 in 2015. The overall MIR for individualswith melanoma decreased from 0.26 to 0.10 in 2015, and from0.37 to 0.12 among individuals ≥65, respectively (Figure 5D).Taken together, these data show substantially different trends ofaggressiveness, historically and currently, with brain cancer andother CNS cancers demonstrating a substantial increase in overallincidence, mortality, and MIR among the elderly portion of theUnited States population.

FIGURE 5 | Mortality to incidence ratio for brain, breast, pancreatic, andmelanoma malignancies over time. The calculated mortality to incidence ratioof (A) brain and other CNS, (B) breast, (C) pancreatic, and (D) melanomamalignancies for each year calculated from SEER incidence and mortalitydata. Instances where the ratio is over 1.0 are due to the lag time betweendiagnosis and mortality in GBM and pancreatic cancer.

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The Incidence and Mortality ofIndividuals With GBM Is ProgressivelyEnriched With AgingGlioblastoma (GBM; grade IV) is the most common primarymalignant brain tumor in adults and accounts for 54% ofmalignant glioma diagnoses (Wen and Kesari, 2008). Amongtotal glioma incidence, the rate of a GBM diagnosis issubstantially higher as compared with grade II and grade IIIglioma (Figure 6A). The rate of a GBM patient diagnosis andmortality is enriched among the elderly population (Figure 6)and the MIR for individuals ≥65 years of age is 1.00.

Not only is GBM enriched among the elderly population,but the prognosis for these patients is substantially worse ascompared with individuals <65 years of age. We analyzed overallsurvival and expression data from the cancer genome atlas(TCGA) for individuals with GBM who possessed correspondingpatient data, expression data, and a reported IDH status (n = 144).There were only 8 patients within the dataset reporting thepresence of mutant IDH (mIDH), all of which were <65 yearsof age. There is a significantly shorter mOS of 11.3 monthsamong individuals≥65 years of age as compared with individuals<65 years of age (14.5 months; P = 0.019) (Figure 7A). Incontrast, mOS for the eight individuals with mIDH GBM is astriking 27.9 months. Intratumoral gene expression levels forthe immunosuppressive enzyme, IDO1, is similar for individuals<65 and ≥65 with wild-type IDH status, and significantlydecreased among mIDH GBM (Figures 7B,C) as was previously

FIGURE 6 | The incidence and mortality rates for glioblastoma (GBM) patientsare enriched among the elderly population. (A) SEER incidence rates (per100,000) for grade II glioma, grade III glioma, and GBM (grade IV glioma; allleft axis) compared to the percent of the total population represented for eachage group. Grade II and III glioma incidence rates calculated by SEER were>0.015 (per 100,000) for each age category. (B) The SEER mortality rate forGBM as compared to the percent of the total population represented for eachage group. Incidence and mortality data is represented as a summation forthe years 2011–2015.

FIGURE 7 | Prognosis is worse for individuals with GBM ≥65. Analysis ofGBM data for individuals with corresponding patient data, gene expression(HiSeq) data, and a reported mIDH1/2 status from the TCGA. (A) Survival dataof individuals with wild-type IDH1/2 <65 (n = 84), ≥65 (n = 52), and thosewith mIDH1/2 (n = 8). IDO1 gene expression (B) dot plot and (C) bar graph ofindividuals with wild-type IDH1/2 <65 (blue), ≥65 (red), and those withmIDH1/2 (green). (D) The percentage of GBM diagnoses within each agecategory for individuals with wtIDH1/2 (blue) and mIDH1/2 (green). (E) Survivaltime in days of individuals diagnosed in each age category. ∗P < 0.05.

reported (Zhai et al., 2017). While a majority of individuals withwild-type IDH GBM were diagnosed later in life, 50 percent ofindividuals with mIDH GBM were diagnosed before the age of35 (Figure 7D). Not surprisingly, overall survival time decreasedwith progressively increasing age among individuals with GBM(Figure 7E). Taken together, the data indicate that individualswith a wild-type IDH GBM, which constitute the majority of newdiagnoses, possess substantial comorbidity with advanced aging.

The Median Age of a GBM PatientDiagnosis Is Coincident With EnhancedGlobal ImmunosuppressionAlthough GBM is generally considered as a disease initiatedby genetic mutations, it may also arise due to immunologicaldysfunction (Shurin, 2012); although it is not clear whichevent(s) precede(s) the other in contributing to GBM cell

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initiation/outgrowth. While immunosuppression is routinelyanalyzed in GBM patients following a diagnosis (Beatty andGladney, 2015), few studies have prospectively investigatedthe correlation between progressive aging and the level ofimmunosuppression in human subjects. With the hypothesis thatthe suppression of the normal immune system contributes tothe microenvironment necessary to facilitate GBM cell initiation,the 10k Immunomes database was profiled for immune systemstatus among healthy human subjects. There were substantialchanges in the peripheral immune system associated with aging.While there were no noticeable trends among total lymphocytes(Figure 8A) and CD19+ B cell (Figure 8B) levels, analysisof T cell subpopulations revealed an interesting phenotypictrend. As demonstrated in Figure 8C, CD4+ T cell levels aremaximal in the 60–69 age range. Drilling down into the CD4+T cell subset revealed that the maximal increase was primarilyattributable to the increase of immunosuppressive regulatoryT cells (Tregs; CD3+CD4+FoxP3+) within the same 60–69age group (Figure 8D). Strikingly, the cytotoxic CD8+ T cellscoincidently decreased with progressive aging (Figure 8E). Sincea high CD8+ T/Treg ratio is associated with improved overallsurvival, (Yue et al., 2014; Shang et al., 2015) and because there isa decreased CD8+ T/Treg ratio during advanced aging of normalhuman healthy individuals that is maximal in the 60–69 agegroup, the data collectively demonstrate a trend for increasedimmunosuppression in the peripheral blood coincident with themedian age of a GBM patient diagnosis.

We previously demonstrated that immunosuppressive IDO1is significantly increased in the normal healthy brain of72–74 week old mice as compared with young 6–8 week

old counterparts; independent of tumor burden (Ladomerskyet al., 2016, 2018). To determine whether this observationis generalizable to humans, RNA-sequencing data from theGTEx database was analyzed for mRNA expression levelsof established immunoregulatory genes in normal humanbrain (Figure 9A). Similar to our analysis of increased IDO1expression levels in the brain of mice during advanced aging,IDO1 is also increased in the normal human brain amongthe 60–69 age group as compared to human subjects inthe 50–59 age group (P = 0.02; Figure 9A; SupplementaryFigure S1). Although this trend remained when comparingthe 60–69 age group to other age cohorts, it did not achievestatistical significance due to insufficient human samples forcomparison. Similar to increased IDO1 mRNA expression, geneexpression for immunosuppressive PD-L1 was also highestin the 60–69 year old age group as compared with the20–29 year old age group (P = 0.01). Unexpectedly, CD11cmRNA levels, a marker traditionally associated with immunesentinel dendritic cells, was also increased in the 60–69 agegroup as compared with human subjects in the 50–59 agegroup (P = 0.02; Figure 9A). Interestingly, samples containingthe highest IDO1 gene expression (Figure 9A; green) alsoshowed high PD-L1 and CD11c levels, possibly suggesting animmunosuppressive phenotype with dendritic cell accumulationin those samples. In contrast to the enrichment of selectimmunosuppressive factors significantly increased in the normalhuman brain, IDO1 mRNA levels do not significantly changein pancreatic, skin, or thyroid tissues across age groups(Figure 9B; Supplementary Figure S2), nor any other humantissue analyzable in the GTEx database. Collectively, these

FIGURE 8 | The peripheral blood CD8+ T/Treg ratio decreases with progressive aging in healthy human subjects. 10k immunomes CyTOF data from the peripheralblood of healthy patients analyzed for (A) total lymphocytes, (B) B cells, (C) CD4+ T cells, (D) CD8+ T cells, (E) Tregs (CD4+CD25+FoxP3+), and (F) CD8+ T/Tregratio. Data was analyzed among age groups including 20–29 (n = 110), 30–39 (n = 14), 40–49 (n = 76), 50–59 (n = 78), 60–69 (n = 72), 70–79 (n = 57), 80+(n = 127). ∗P < 0.05; ∗∗P < 0.01; ∗∗∗P < 0.001; ∗∗∗∗P < 0.0001.

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FIGURE 9 | Gene expression for immunosuppressive and immunoregulatory factors increase in the normal human brain with aging. (A) GTEx gene expressionanalysis of IDO1, TDO2, CD3ε, PD-L1, CTLA-4, and CD11c in the brain. Data was analyzed among age groups including 20–29 (n = 38), 30–39 (n = 19), 40–49(n = 123), 50–59 (n = 386), 60–69 (n = 549), 70–79 (n = 37). Green circles across gene panels share the same sample IDs. (B) GTEx gene expression analysis ofIDO1 in the normal human pancreas (n = 167), skin (n = 812), and thyroid (n = 280). ∗P < 0.05.

data confirm that systemic and brain-specific immunoregulatoryfactors favoring immunosuppression are maximal during thetime frame associated with the median age of a GBMpatient diagnosis.

DISCUSSION

The Director of the National Cancer Institute, Dr. Normal E.Sharpless, M.D., formerly an investigator within the NationalInstitute of Aging, has highlighted the importance of therelationship between cancer and aging (Sharpless, 2018).Cancer is a disease enriched among the elderly and is oftenassociated with deficits in normal immunosurveillance (Dunnet al., 2004; Zhong et al., 2016). Aging is associated withprogressive immunological changes throughout the body,including involution of the thymus, a critical site for pre-Tcell education and development into mature naïve T cells,a shift of the circulating T cell repertoire from a naïve tomemory phenotype, an absolute decrease in the number of

naïve T and B cells, a reduction in cytokine signaling, andreduced uptake of antigens and/or microbes by dendriticcells. To further understand the immunological changes thatoccur during aging, and whether these differences providefor preventative or therapeutic applications to individualsthat will be diagnosed with malignant primary brain cancer,we comprehensively studied multiple bioinformatic databaserepositories for aging-related associations between GBMpatient incidence and mortality, changes in the peripheralimmune response of normal healthy human subjects, andgene expression throughout the normal healthy human bodyfor immunoregulatory factors. Accordingly, the scientificpremise of our investigation aimed to identify meaningfulrelationships between the ages of a GBM patient diagnosis,with differences in the immune system that are potentiallytherapeutically targetable.

During our investigation, we discovered that the United Statesrate of incidence and mortality for brain and other CNS malig-nancies has been rising over the past 40 years, and due to its

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enrichment among the elderly population, may reflect theincreased number of individuals that have a higher propensityfor brain cancer diagnoses as compared with younger subjects(Figures 1, 3, 4, 5). This differs from the trend for allmalignancies, where the incidence and mortality rates declinebetween the years 1995 and 2015 (Figures 1C,D). This maybe due to an increase in the number of effective therapies fortreating other types of cancer, as well as improved preventionand detection techniques for high incidence malignanciesincluding breast cancer and melanoma. According to a 2018projection by the NCI (see text footnote 3), brain and otherCNS malignancies have the third highest mortality:incidence(MIR) ratio when comparing across major cancer subtypes,which reflects a high degree of aggressiveness. Strikingly,brain cancer and other CNS malignancies were predictedto be the most expensive cancer on a per patient basis inthe year 2018 (Figure 2). Further investigation of gliomaincidence data revealed that GBM diagnoses are highlyenriched among the elderly. When the mortality data wasaccounted for, the MIR for individuals with GBM betweenthe years 2011–2015 was 1.00 (Figure 6). Analysis of TCGAdata revealed that not only are the elderly at a higherrisk for GBM, but this cohort of individuals also havea significantly worse prognosis when diagnosed with GBM(Figure 7). Further evaluation revealed a peak GBM patientincidence/mortality rate that corresponded to the maximal levelsof immunosuppressive IDO1 and PD-L1 mRNA expressionin the CNS, as well as immunosuppressive peripheral Tregabundance (Figures 8, 9). Interestingly, the CD4+FoxP3− T cellswere less affected by aging (Figure 8). A hypothesis for theincreased immunosuppressive markers in the 60–69 age group,but not among even older age groups, is that only a subsetof individuals experience this increase. It’s also possible thatthe transient increase in local immunosuppression synergizeswith peripheral mechanisms during a timeframe of substantialhormonal imbalance (i.e., menopause). Interestingly, CD11cmRNA expression also increased in the elderly human brain,which may be associated with the accumulation of brain-resident cells (Bulloch et al., 2008; D’Agostino et al., 2012;Kaunzner et al., 2012).

Since cancer is enriched among the elderly, combinedwith the increasing size of the population with advanced age(Figures 1A,B), it was surprising to find that not all malignancieshad an associated increased mortality rate when comparingdata between 1975 and 2015. Several hypotheses may explainthe rise in mortality of primary malignant brain cancer ascompared to other malignancies including: (i) the lack of effectivetreatment options for patients diagnosed with incurable braintumors as compared to therapeutic improvements for non-CNSmalignancies; (ii) a steady enrichment in factors that promotethe development of more malignant primary brain tumors; (iii)a more aggressive natural history in established primary braintumors; (iv) an absolute increase in the number of elderlyindividuals that have a higher chance for developing primarymalignant glioma; (v) an increase over time of elderly patientsdiagnosed with malignant brain tumors due to more aggressivepatient work ups providing for a larger pool of individuals

contributing to the mortality statistics; or (vi) a combination ofall stated potential conferring factors.

The CNS is a potently immunosuppressive andimmunospecialized organ, as compared with peripheral tissues(Carson et al., 2006). Through normal human subject geneexpression analysis, we found increased immunosuppressiveIDO1 in the brain that was maximally enhanced in the60–69 year old subgroup, which is complementary to ourprevious work demonstrating increased IDO1 in the normalbrain of mice during advanced aging (Ladomersky et al., 2016,2018). Unexpectedly, IDO1 enzyme activity was not increasedin the aged brain, questioning the functionality of the increasedbrain IDO1 expression. Recent studies in our laboratory andothers have hypothesized that the immunosuppressive functionof IDO1 may be, in-part, independent of its associated enzymeactivity (Pallotta et al., 2011; Wainwright et al., 2012; Zhaiet al., 2017). Although additional studies are required to fullyunderstand the relationship between increased human brainIDO1 expression and advanced aging, a hypothesis that currentlyfits the available data is that IDO1 expression increases CNSimmunosuppression through non-enzyme activity. Whetherthe basal increase of brain IDO1 expression contributes to themicroenvironment required for GBM cell initiation is also anintriguing consideration.

Another factor for considering the effects of aging onimmunosuppression and GBM onset is the differences betweenprimary, or de novo GBM, and secondary GBM; the latter ofwhich develops from lower grade II or III glioma. PrimaryGBM represents a majority of all malignant glioma cases(>90%) (Ohgaki and Kleihues, 2007). Primary GBM is routinelyassociated with mutation of PTEN (Ohgaki et al., 2004), EGFRamplification (Ekstrand et al., 1992), and p16INK4a deletion(Biernat et al., 1997). Secondary GBM, which accounts for<10% of GBM diagnoses, tends to arise in younger individualsand is characterized by the presence of mutated isocitratedehydrogenase (mIDH) and TP53 (Kohanbash et al., 2017). Themedian age of diagnosis for patients with a secondary GBMis ∼45 years old (Ohgaki and Kleihues, 2005). The differentages at which primary and secondary GBMs arise may reflectdifferent microenvironmental CNS conditions that contribute toGBM cell initiation. Highlighting this plurality, GBM presentingwith a mIDH is often associated with immune cell exclusionand almost totally absent of tumor cell-killing cytolytic CD8+ Tcells (Kohanbash et al., 2017). Despite the minimal infiltrationof immune cells, the presence of mIDH is associated with afavorable prognosis in GBM, as compared with primary GBMthat predominantly contain wild-type IDH (Yan et al., 2009).

Our comprehensive analysis across multiple databasesprovides a unique perspective for assessing the risk of GBM inthe elderly. While the data show interesting trends, a prospectiveanalysis of a large patient cohort is warranted to validate thehypotheticals proposed by our current study. Some of theseconsiderations will be built into an upcoming prospectivephase I/II trial evaluating newly diagnosed GBM patients,before and after treatment with standard radiation, nivolumab(anti-PD-1 mAb) and BMS986205 (IDO1 enzyme inhibitor),led by our group. Further analysis of TCGA survival data

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allows for a valuable comparison of prognostic criteriabetween elderly and younger individuals, but still does notaccount for major differences of tumor biology. Future studiesaimed at evaluating aging-related mechanisms and increasedimmunosuppression will allow for a validation of whether thereare therapeutically targetable changes that potentially preventbrain tumor incidence and/or enhance the effectiveness ofimmunotherapeutic treatments.

Taken together, these data suggest that immunosuppressivechanges in the brain are affected by processes mediated byaging and may contribute to the significantly increased braincancer mortality rate enriched within the elderly population.The median age of a GBM diagnosis coincides with animmunosuppressive phenotype in the peripheral blood andinside the brain parenchyma. Potentially, subsets of individualswith altered expression of immunoregulatory genes possess anenhanced risk for developing GBM. A high priority must beplaced on determining whether these gene expression changescontribute to tumor cell initiation and/or progression, as wellas the ability to detect increases in CNS immunosuppressionthrough a peripheral (i.e., outside of the CNS) biomarker. Furtherresearch in this area will not only allow for a better understandingof elderly GBM patient treatment, but also potentially contributeto the ability of identifying individuals at a higher risk fordeveloping the terminal disease.

DATA AVAILABILITY

All datasets generated for this study are included in themanuscript and/or the Supplementary Files.

AUTHOR CONTRIBUTIONS

EL performed a majority of the data mining associated withthis submitted work. Data analysis was performed by EL, withstatistical input from DS. All data were reviewed by DMS, MK,EAH, ETB, SO, LZ, KLL, JC, JAS, JDW, BZ, and RVL. EL andDAW prepared the figures and wrote the manuscript.

FUNDING

EL was supported by PHS grant number T32CA070085. DAWwas supported by PHS grant number R01NS097851 awardedby the NIH/NINDS, United States Department of Health andHuman Services. RVL and DAW are supported by PHS grantnumber P50CA221747 awarded by the NIH/NCI, United StatesDepartment of Health and Human Services; the Gail BoyterMagness Foundation; the Grace Giving Foundation. ETB wassupported by PHS grant 5R50CA221848-02 awarded by the NCI.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be foundonline at: https://www.frontiersin.org/articles/10.3389/fphar.2019.00200/full#supplementary-material

FIGURE S1 | GTEx Analysis. GTEx gene expression analysis of IDO1, TDO2,CD3e, and PD-L1 in the brain, pancreas, skin, and thyroid.

FIGURE S2 | GTEx Analysis. GTEx gene expression analysis of CTLA-4, CD11c,PTGS2, and SLC6A4 in the brain, pancreas, skin, and thyroid.

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Conflict of Interest Statement: The authors declare that the research wasconducted in the absence of any commercial or financial relationships that couldbe construed as a potential conflict of interest.

Copyright © 2019 Ladomersky, Scholtens, Kocherginsky, Hibler, Bartom, Otto-Meyer, Zhai, Lauing, Choi, Sosman, Wu, Zhang, Lukas and Wainwright. This is anopen-access article distributed under the terms of the Creative Commons AttributionLicense (CC BY). The use, distribution or reproduction in other forums is permitted,provided the original author(s) and the copyright owner(s) are credited and that theoriginal publication in this journal is cited, in accordance with accepted academicpractice. No use, distribution or reproduction is permitted which does not complywith these terms.

Frontiers in Pharmacology | www.frontiersin.org 13 February 2019 | Volume 10 | Article 200